Example3.qmd

```{r setup, include=FALSE}
#Example 3
library(here)
library(ptaxsim)
setwd("/users/monicanimmagadda/ptax-project")
source('ptax_sim_functions.R')
source('maps.R')
library(latex2exp)

pin_14 <- "17313070150000"
year = 2021
tax_code = lookup_tax_code(year, pin_14)
agencies = lookup_agency(year, tax_code)

agency_list <- get_highest_ext(agencies)
sec_school <- select_second_school(agencies)
districts <- append(agency_list$agency_name, sec_school)
agency_df <- filter(agencies, agencies$agency_name %in% districts)
col_names <- c("district", "type", "levy", "base", "rate", "av", "eav", "property_tax")
df <- data.frame(matrix(ncol = 8, nrow = 0))
colnames(df) <- col_names

df <- populate_df(df, districts, year, pin_14)
df <- format_df(df)

df$agency_minor_type <- factor(df$agency_minor_type, levels = c("COOK", "MUNI", "SCHOOL", "SCHOOL1", "WATER"))
df <- df[order(df$agency_minor_type), ]

not_top_agencies <- filter(agencies, !(agencies$agency_name %in% districts))
df_extra <- data.frame(matrix(ncol=8, nrow=0))
colnames(df_extra) <- col_names
df_extra <- populate_df(df_extra, not_top_agencies$agency_name, year, pin_14)
df_extra <- format_df(df_extra)

dist_1 <- df$district[1]
dist_2 <- df$district[2]
dist_3 <- df$district[3]
dist_4 <- df$district[4]
dist_5 <- df$district[5]
```

![](chicago.jpeg)

## Introduction

Cook County relies on property taxes to serve its residents. Find out how your property tax is calculated by reading our guide.

The Cook County Assessor's Office, the State, and county taxing districts (schools, parks, etc.) work together to calculate fair property taxes for residents.

Let's talk a more thorough look at this outline below.

## What are property taxes?

Property tax is a tax assessed on real estate by a government authority, typically based on the value of the property. 
The tax is used to fund public services such as schools, roads, and emergency services in the local area.

Property owners must pay this tax annually, regardless of whether they own a residential or commercial property.

![](outline_pic_2.png)

## How do property taxes work?

::: columns
::: {.column width="50%"}
Let's take a look at a **property tax bill**.

This is confusing. Let's zoom out and explain what this all means and how we calculate these numbers.

First, let's take a look at tax rates...
:::

::: {.column width="50%"}

:::
:::

## How do we calculate tax rate?

We take a look at the [**`r dist_1`**]{style="color:purple;"} (as colored in purple) 

```{r, echo=FALSE}
(p1_1_cook <- ggplot() +
  geom_sf(data = shp_bnd_cook) + 
  geom_sf(data = shp_bnd_chicago) +
  geom_sf(data = shp_bnd_riverside, fill = "#6a3d9a") +
  theme_void())
```

We zoom in to show the taxing district of the [**`r dist_1`**]{style="color:purple;"} 


Each year, this taxing district sets a [**levy**]{style="color:purple;"} . This is the amount it needs to collect that year in order to provide services. (This was `r df$levy[1]` in 2021.)



Let's zoom in even further. If you are a [**property owner**]{style="color:red;"} in [**`r dist_1`**]{style="color:purple;"} of a home worth [**`r df$av[1]`**]{style="color:red;"}, your taxable value (EAV) is [**`r df$eav[1]`**]{style="color:red;"}.

```{r, echo=FALSE}
(p1_1_riverside_pin <- ggplot() +
  annotation_map_tile("cartolight", zoomin = -1) +
  annotation_scale(location = "br", unit_category = "imperial") +
  geom_sf(
    data = shp_bnd_example_pin,
    fill = "#e41a1c"
  ) +
  geom_sf(
    data = shp_bnd_riverside,
    color = "#6a3d9a",
    fill = "transparent",
    linewidth = 1
  ) +
  theme_void())
```

The total taxable value of all properties in [**`r dist_1`**]{style="color:purple;"} make up its [**tax base**]{style="color:fuchsia;"} ([**`r dist_1`'s**]{style="color:purple;"} tax base is [**`r df$base[1]`**]{style="color:fuchsia;"} )

To divide the cost of services among all property owners, [**`r dist_1`**]{style="color:purple;"} sets its local tax rate equal to: $\mathrm{{Levy \over Base} = Tax \hspace{0.1cm} rate}$
```{r, echo=FALSE}

(p1_1_riverside_base <- ggplot() +
  annotation_map_tile("cartolight", zoomin = -1) +
  annotation_scale(location = "br", unit_category = "imperial") +
  geom_sf(
    data = shp_bnd_riverside_pins,
    fill = "#cab2d6",
    color = "white",
    alpha = 0.75
  ) +
  geom_sf(
    data = shp_bnd_riverside,
    color = "#6a3d9a",
    fill = "transparent",
    linewidth = 1
  ) +
  geom_sf(
    data = shp_bnd_example_pin,
    fill = "#e41a1c"
  ) +
  theme_void())
```

Using real numbers, the tax rate equals: $\mathrm{{5.9M \over 320M} = 1.84%}$

## How much of this tax rate do you pay?

This **rate** is then applied to your **property's taxable value**:

The rate and tax amount then appear as a line item on your tax bill:

| District                                          | Levy/Base                     | 2021 Rate          | 2021 Tax               |
|-----------------------|-----------------|-----------------|-----------------|
| [**`r dist_1`**]{style="color:purple;"} | `r df$levy[1]`/`r df$base[1]` | `r df$rate[1]` | `r df$property_tax[1]` |

But [**`r dist_1`**]{style="color:purple;"} isn't the only local government you must pay taxes to...
```{r, echo=FALSE}

(p1_1_riverside_base <- ggplot() +
  annotation_map_tile("cartolight", zoomin = -1) +
  annotation_scale(location = "br", unit_category = "imperial") +
  geom_sf(
    data = shp_bnd_riverside_pins,
    fill = "#cab2d6",
    color = "white",
    alpha = 0.75
  ) +
  geom_sf(
    data = shp_bnd_riverside,
    color = "#6a3d9a",
    fill = "transparent",
    linewidth = 1
  ) +
  geom_sf(
    data = shp_bnd_example_pin,
    fill = "#e41a1c"
  ) +
  theme_void())
```
There's also [**`r dist_2`**]{style="color:orange;"} , which sets its own **levy** of [**`r df$levy[2]`**]{style="color:orange;"}
```{r, echo=FALSE}
(p1_1_elem_dist_levy <- ggplot() +
  annotation_map_tile("cartolight", zoomin = -1) +
  annotation_scale(location = "br", unit_category = "imperial") +
  geom_sf(
    data = shp_bnd_riverside_pins,
    fill = "#cab2d6",
    color = "white"
  ) +
  geom_sf(
    data = shp_bnd_riverside,
    color = "#984ea3",
    fill = "transparent",
    linewidth = 1
  ) +
  geom_sf(
    data = shp_bnd_elem_dist,
    color = "#ff7f00",
    fill = "transparent",
    linewidth = 1
  ) +
  geom_sf(
    data = shp_bnd_example_pin,
    fill = "#e41a1c"
  ) +
  theme_void())
```

It adds another line item to your tax bill...

| District                                                   | Levy/Base                         | 2021 Rate          | 2021 Tax               |
|-----------------------|-----------------|-----------------|-----------------|
| [**`r dist_1`**]{style="color:purple;"}          | `r df %>% filter(district==dist_1) %>% select(levy)`/ `r df %>% filter(district==dist_1) %>% select(base)`     | `r df %>% filter(district==dist_1) %>% select(rate)` | `r df %>% filter(district==dist_1) %>% select(property_tax)` |
| [**`r dist_2`**]{style="color:orange;"} | `r df %>% filter(district==dist_2) %>% select(levy)`/`r df %>% filter(district==dist_2) %>% select(base)` | `r df %>% filter(district==dist_2) %>% select(rate)`             | `r df %>% filter(district==dist_2) %>% select(property_tax)`               |

```{r, echo=FALSE}
(p1_1_elem_dist_base <- ggplot() +
  annotation_map_tile("cartolight", zoomin = -1) +
  annotation_scale(location = "br", unit_category = "imperial") +
  geom_sf(
    data = shp_bnd_elem_dist_pins,
    fill = "#fdbf6f",
    color = "white"
  ) +
  geom_sf(
    data = shp_bnd_riverside_pins,
    fill = "#cab2d6",
    color = "white"
  ) +
  geom_sf(
    data = shp_bnd_riverside,
    color = "#984ea3",
    fill = "transparent",
    linewidth = 1
  ) +
  geom_sf(
    data = shp_bnd_elem_dist,
    color = "#ff7f00",
    fill = "transparent",
    linewidth = 1
  ) +
  geom_sf(
    data = shp_bnd_example_pin,
    fill = "#e41a1c"
  ) +
  theme_void())
```
You must also pay [**`r dist_3`**]{style="color:green;"}

```{r, echo=FALSE}
(p1_1_township_base <- ggplot() +
  annotation_map_tile("cartolight", zoomin = -1) +
  annotation_scale(location = "br", unit_category = "imperial") +
  geom_sf(
    data = shp_bnd_riverside_pins,
    fill = "#cab2d6",
    color = "white"
  ) +
  geom_sf(
    data = shp_bnd_elem_dist_pins,
    fill = "#fdbf6f",
    color = "white"
  ) +
  geom_sf(
    data = shp_bnd_township_pins,
    fill = "#b2df8a",
    color = "white"
  ) +
  geom_sf(
    data = shp_bnd_riverside,
    color = "#984ea3",
    fill = "transparent",
    linewidth = 1
  ) +
  geom_sf(
    data = shp_bnd_elem_dist,
    color = "#ff7f00",
    fill = "transparent",
    linewidth = 1
  ) +
  geom_sf(
    data = shp_bnd_township,
    color = "#33a02c",
    fill = "transparent",
    linewidth = 1
  ) +
  geom_sf(
    data = shp_bnd_example_pin,
    fill = "#e41a1c"
  ) +
  theme_void())

library(latex2exp)

```
| District                                                   | Levy/Base                         | 2021 Rate | 2021 Tax |
|-----------------------|-----------------|-----------------|-----------------|
| [**`r dist_1`**]{style="color:purple;"} | `r df %>% filter(district==dist_1) %>% select(levy)`/ `r df %>% filter(district==dist_1) %>% select(base)` | `r df %>% filter(district==dist_1) %>% select(rate)` | `r df %>% filter(district==dist_1) %>% select(property_tax)` |
| [**`r dist_2`**]{style="color:orange;"} | `r df %>% filter(district==dist_2) %>% select(levy)`/ `r df %>% filter(district==dist_2) %>% select(base)` | `r df %>% filter(district==dist_2) %>% select(rate)` | `r df %>% filter(district==dist_2) %>% select(property_tax)` |
| [**`r dist_3`**]{style="color:green;"} | `r df %>% filter(district==dist_3) %>% select(levy)`/ `r df %>% filter(district==dist_3) %>% select(base)` | `r df %>% filter(district==dist_3) %>% select(rate)` | `r df %>% filter(district==dist_3) %>% select(property_tax)` |






And [**`r dist_4`**]{style="color:blue;"}
```{r, echo=FALSE}
(p1_1_hs_dist_base <- ggplot() +
  annotation_map_tile("cartolight", zoomin = -1) +
  annotation_scale(location = "br", unit_category = "imperial") +
  geom_sf(
    data = shp_bnd_riverside_pins,
    fill = "#cab2d6",
    color = "white"
  ) +
  geom_sf(
    data = shp_bnd_elem_dist_pins,
    fill = "#fdbf6f",
    color = "white"
  ) +
  geom_sf(
    data = shp_bnd_township_pins,
    fill = "#b2df8a",
    color = "white"
  ) +
  geom_sf(
    data = shp_bnd_hs_dist_pins,
    fill = "#a6cee3",
    color = "white"
  ) +
  geom_sf(
    data = shp_bnd_riverside,
    color = "#984ea3",
    fill = "transparent",
    linewidth = 1
  ) +
  geom_sf(
    data = shp_bnd_elem_dist,
    color = "#ff7f00",
    fill = "transparent",
    linewidth = 1
  ) +
  geom_sf(
    data = shp_bnd_township,
    color = "#33a02c",
    fill = "transparent",
    linewidth = 1
  ) +
  geom_sf(
    data = shp_bnd_hs_dist,
    color = "#1f78b4",
    fill = "transparent",
    linewidth = 1
  ) +
  geom_sf(
    data = shp_bnd_example_pin,
    fill = "#e41a1c"
  ) +
  coord_sf(clip = "off") +
  theme_void())
```

| District                                                       | Levy/Base                         | 2021 Rate | 2021 Tax |
|-----------------------|----------------|----------------|----------------|
| [**`r dist_1`**]{style="color:purple;"} | `r df %>% filter(district==dist_1) %>% select(levy)`/ `r df %>% filter(district==dist_1) %>% select(base)` | `r df %>% filter(district==dist_1) %>% select(rate)` | `r df %>% filter(district==dist_1) %>% select(property_tax)` |
| [**`r dist_2`**]{style="color:orange;"} | `r df %>% filter(district==dist_2) %>% select(levy)`/ `r df %>% filter(district==dist_2) %>% select(base)` | `r df %>% filter(district==dist_2) %>% select(rate)` | `r df %>% filter(district==dist_2) %>% select(property_tax)` |
| [**`r dist_3`**]{style="color:green;"} | `r df %>% filter(district==dist_3) %>% select(levy)`/ `r df %>% filter(district==dist_3) %>% select(base)` | `r df %>% filter(district==dist_3) %>% select(rate)` | `r df %>% filter(district==dist_3) %>% select(property_tax)` |
| [**`r dist_4`**]{style="color:blue;"} |`r df %>% filter(district==dist_4) %>% select(levy)`/`r df %>% filter(district==dist_4) %>% select(base)`    | `r df %>% filter(district==dist_4) %>% select(rate)`| `r df %>% filter(district==dist_4) %>% select(property_tax)` |

And the [**`r dist_5`**]{style="color:brown;"}
```{r, echo=FALSE}
(p1_1_cc_dist <- ggplot() +
  annotation_map_tile("cartolight", zoomin = -1) +
  annotation_scale(location = "br", unit_category = "imperial") +
  geom_sf(
    data = shp_bnd_cc_dist,
    color = "#e31a1c",
    fill = "#fb9a99",
    linewidth = 1,
    alpha = 0.2
  ) +
  geom_sf(
    data = shp_bnd_hs_diff,
    color = "#1f78b4",
    fill = "#a6cee3",
    linewidth = 1,
    alpha = 0.2
  ) +
  geom_sf(
    data = shp_bnd_township_diff,
    color = "#33a02c",
    fill = "#b2df8a",
    linewidth = 1,
    alpha = 0.2
  ) +
  geom_sf(
    data = shp_bnd_elem_diff,
    color = "#ff7f00",
    fill = "#fdbf6f",
    linewidth = 1,
    alpha = 0.2
  ) +
  geom_sf(
    data = shp_bnd_riverside,
    color = "#984ea3",
    fill = "#cab2d6",
    linewidth = 1,
    alpha = 0.2
  ) +
  coord_sf(clip = "off") +
  theme_void())
```
| District                                                       | Levy/Base                         | 2021 Rate | 2021 Tax |
|-----------------------|----------------|----------------|----------------|
| [**`r dist_1`**]{style="color:purple;"} | `r df %>% filter(district==dist_1) %>% select(levy)`/ `r df %>% filter(district==dist_1) %>% select(base)` | `r df %>% filter(district==dist_1) %>% select(rate)` | `r df %>% filter(district==dist_1) %>% select(property_tax)` |
| [**`r dist_2`**]{style="color:orange;"} | `r df %>% filter(district==dist_2) %>% select(levy)`/ `r df %>% filter(district==dist_2) %>% select(base)` | `r df %>% filter(district==dist_2) %>% select(rate)` | `r df %>% filter(district==dist_2) %>% select(property_tax)` |
| [**`r dist_3`**]{style="color:green;"} | `r df %>% filter(district==dist_3) %>% select(levy)`/ `r df %>% filter(district==dist_3) %>% select(base)` | `r df %>% filter(district==dist_3) %>% select(rate)` | `r df %>% filter(district==dist_3) %>% select(property_tax)` |
| [**`r dist_4`**]{style="color:blue;"} |`r df %>% filter(district==dist_4) %>% select(levy)`/`r df %>% filter(district==dist_4) %>% select(base)`    | `r df %>% filter(district==dist_4) %>% select(rate)`| `r df %>% filter(district==dist_4) %>% select(property_tax)` |
| [**`r dist_5`**]{style="color:blue;"} |`r df %>% filter(district==dist_5) %>% select(levy)`/`r df %>% filter(district==dist_5) %>% select(base)`    | `r df %>% filter(district==dist_5) %>% select(rate)`| `r df %>% filter(district==dist_5) %>% select(property_tax)` |

And so on:

| District                                                       | Levy/Base                         | 2021 Rate | 2021 Tax |
|-----------------------|-----------------|-----------------|-----------------|
| [**`r dist_1`**]{style="color:purple;"} | `r df %>% filter(district==dist_1) %>% select(levy)` / `r df %>% filter(district==dist_1) %>% select(base)` | `r df %>% filter(district==dist_1) %>% select(rate)` | `r df %>% filter(district==dist_1) %>% select(property_tax)` |
| [**`r dist_2`**]{style="color:orange;"} | `r df %>% filter(district==dist_2) %>% select(levy)` / `r df %>% filter(district==dist_2) %>% select(base)` | `r df %>% filter(district==dist_2) %>% select(rate)` | `r df %>% filter(district==dist_2) %>% select(property_tax)` |
| [**`r dist_3`**]{style="color:green;"} | `r df %>% filter(district==dist_3) %>% select(levy)` / `r df %>% filter(district==dist_3) %>% select(base)` | `r df %>% filter(district==dist_3) %>% select(rate)` | `r df %>% filter(district==dist_3) %>% select(property_tax)` |
| [**`r dist_4`**]{style="color:blue;"} | `r df %>% filter(district==dist_4) %>% select(levy)` / `r df %>% filter(district==dist_4) %>% select(base)` | `r df %>% filter(district==dist_4) %>% select(rate)` | `r df %>% filter(district==dist_4) %>% select(property_tax)` |
| [**`r dist_5`**]{style="color:red;"} | `r df %>% filter(district==dist_5) %>% select(levy)` / `r df %>% filter(district==dist_5) %>% select(base)` | `r df %>% filter(district==dist_5) %>% select(rate)` | `r df %>% filter(district==dist_5) %>% select(property_tax)` |
| [**`r df_extra$district[1]`**]{style="color:navy;"} | `r df_extra %>% filter(district==df_extra$district[1]) %>% select(levy)` / `r df_extra %>% filter(district==df_extra$district[1]) %>% select(base)` | `r df_extra %>% filter(district==df_extra$district[1]) %>% select(rate)` | `r df_extra %>% filter(district==df_extra$district[1]) %>% select(property_tax)` |
| [**`r df_extra$district[2]`**]{style="color:black;"} | `r df_extra %>% filter(district==df_extra$district[2]) %>% select(levy)` / `r df_extra %>% filter(district==df_extra$district[2]) %>% select(base)` | `r df_extra %>% filter(district==df_extra$district[2]) %>% select(rate)` | `r df_extra %>% filter(district==df_extra$district[2]) %>% select(property_tax)` |


## Now let's compare this to the bill above

When reorganized, our bill matches the real bill

Except there are more districts on the real bill...

::: footer
Learn more: [Cook County Assessor's Office](https://www.cookcountyassessor.com/)
:::